fix: Resolve OCR left margin truncation issue
- Add safety padding (50px) around images before preprocessing to protect edge content during deskew rotation - Fix _deskew() to expand canvas during rotation instead of using fixed canvas size with BORDER_REPLICATE (which lost edge content) - Add fallback payment method patterns for truncated text detection (RD→CARD, ARD→CARD, MERAR→NUMERAR) This fixes the issue where text near left edge was being cut off, causing "CARD" to appear as "RD", "SUBTOTAL" as "UBTOTAL", etc. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
@@ -16,6 +16,25 @@ except ImportError:
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class ImagePreprocessor:
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"""Preprocess receipt images for OCR."""
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def _add_safety_padding(self, image: np.ndarray, padding: int = 50) -> np.ndarray:
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"""Add white padding around image to protect edge content during rotation.
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This prevents left/right margin truncation in OCR by ensuring text near
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edges isn't lost during deskew rotation.
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"""
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if len(image.shape) == 2:
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# Grayscale
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return cv2.copyMakeBorder(
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image, padding, padding, padding, padding,
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cv2.BORDER_CONSTANT, value=255
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)
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else:
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# Color (BGR)
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return cv2.copyMakeBorder(
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image, padding, padding, padding, padding,
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cv2.BORDER_CONSTANT, value=(255, 255, 255)
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)
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def load_image(self, path: Path) -> np.ndarray:
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"""Load image from file."""
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image = cv2.imread(str(path))
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@@ -48,16 +67,31 @@ class ImagePreprocessor:
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Light preprocessing for CLEAR images (PDFs, good scans).
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Preserves original quality, only enhances contrast.
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"""
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# 0. Add safety padding to protect edge content during deskew rotation
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image = self._add_safety_padding(image)
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# 1. Grayscale
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if len(image.shape) == 3:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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else:
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gray = image.copy()
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# 2. Resize if too small
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# 2a. Scale DOWN if any side exceeds 4000px (PaddleOCR limit)
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height, width = gray.shape
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max_side = max(height, width)
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if max_side > 4000:
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scale = 4000 / max_side
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gray = cv2.resize(gray, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
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height, width = gray.shape
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# 2b. Scale UP if too small
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if width < 1500:
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scale = 1500 / width
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# Ensure we don't exceed 4000px after upscaling
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new_width = int(width * scale)
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new_height = int(height * scale)
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if max(new_width, new_height) > 4000:
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scale = 4000 / max(new_width, new_height)
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gray = cv2.resize(gray, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
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# 3. Deskew
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@@ -75,16 +109,31 @@ class ImagePreprocessor:
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Heavy preprocessing for FADED thermal receipts.
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Aggressive binarization to recover faded text.
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"""
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# 0. Add safety padding to protect edge content during deskew rotation
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image = self._add_safety_padding(image)
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# 1. Grayscale
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if len(image.shape) == 3:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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else:
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gray = image.copy()
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# 2. Resize if too small (larger = better OCR)
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# 2a. Scale DOWN if any side exceeds 4000px (PaddleOCR limit)
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height, width = gray.shape
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max_side = max(height, width)
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if max_side > 4000:
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scale = 4000 / max_side
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gray = cv2.resize(gray, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
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height, width = gray.shape
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# 2b. Scale UP if too small (larger = better OCR)
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if width < 1500:
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scale = 1500 / width
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# Ensure we don't exceed 4000px after upscaling
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new_width = int(width * scale)
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new_height = int(height * scale)
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if max(new_width, new_height) > 4000:
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scale = 4000 / max(new_width, new_height)
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gray = cv2.resize(gray, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
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# 3. Deskew
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@@ -115,6 +164,51 @@ class ImagePreprocessor:
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return result
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def preprocess_for_tesseract(self, image: np.ndarray) -> np.ndarray:
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"""
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Tesseract-optimized preprocessing.
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Tesseract works best with:
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- Clean black text on white background (binarized)
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- High DPI (scale up small images)
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- Otsu thresholding (better than adaptive for clean documents)
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"""
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# 0. Add safety padding to protect edge content during deskew rotation
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image = self._add_safety_padding(image)
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# 1. Grayscale
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if len(image.shape) == 3:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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else:
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gray = image.copy()
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# 2. Scale for optimal Tesseract (target ~2000px width for receipts)
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height, width = gray.shape
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if width < 2000:
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scale = 2000 / width
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gray = cv2.resize(gray, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
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elif width > 3000:
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scale = 3000 / width
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gray = cv2.resize(gray, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
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# 3. Deskew
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gray = self._deskew(gray)
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# 4. Strong contrast enhancement
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clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
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enhanced = clahe.apply(gray)
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# 5. Denoise before binarization
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denoised = cv2.fastNlMeansDenoising(enhanced, h=10, templateWindowSize=7, searchWindowSize=21)
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# 6. Otsu binarization (better than adaptive for clean PDFs)
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_, binary = cv2.threshold(denoised, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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# 7. Light morphological cleanup
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))
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cleaned = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
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return cleaned
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def get_all_variants(self, image: np.ndarray) -> List[np.ndarray]:
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"""
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Generate 2 preprocessing variants for OCR (fast mode).
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@@ -126,7 +220,11 @@ class ImagePreprocessor:
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]
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def _deskew(self, image: np.ndarray) -> np.ndarray:
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"""Correct image rotation/skew using Hough lines."""
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"""Correct image rotation/skew using Hough lines.
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Uses expanded canvas to preserve all content during rotation,
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preventing left/right margin truncation.
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"""
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edges = cv2.Canny(image, 50, 150, apertureSize=3)
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lines = cv2.HoughLinesP(
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edges, 1, np.pi / 180,
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@@ -153,8 +251,20 @@ class ImagePreprocessor:
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h, w = image.shape[:2]
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center = (w // 2, h // 2)
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M = cv2.getRotationMatrix2D(center, median_angle, 1.0)
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# Calculate new canvas size to fit entire rotated image (prevents edge truncation)
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cos_angle = abs(np.cos(np.radians(median_angle)))
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sin_angle = abs(np.sin(np.radians(median_angle)))
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new_w = int(h * sin_angle + w * cos_angle)
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new_h = int(h * cos_angle + w * sin_angle)
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# Adjust rotation matrix for new canvas center
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M[0, 2] += (new_w - w) / 2
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M[1, 2] += (new_h - h) / 2
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return cv2.warpAffine(
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image, M, (w, h),
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image, M, (new_w, new_h),
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flags=cv2.INTER_CUBIC,
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borderMode=cv2.BORDER_REPLICATE
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borderMode=cv2.BORDER_CONSTANT,
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borderValue=255 # White background (grayscale)
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)
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@@ -23,6 +23,7 @@ class ExtractionResult:
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tva_total: Optional[Decimal] = None
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address: Optional[str] = None
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items_count: Optional[int] = None
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payment_methods: List[dict] = field(default_factory=list) # [{"method":"CARD","amount":Decimal}]
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confidence_amount: float = 0.0
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confidence_date: float = 0.0
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@@ -183,6 +184,24 @@ class ReceiptExtractor:
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(r'(\d{1,2})\s*%\s*:?\s*([\d\s.,]+)', 0.75),
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]
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# Payment method patterns - appears after TOTAL LEI, before TOTAL TVA
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# Format: "CARD: 50.00" or "NUMERAR 100.00" or "PLATA CARD: 50.00"
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PAYMENT_METHOD_PATTERNS = [
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# CARD with amount (high confidence)
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(r'(?:PLATA\s+)?CARD\s*:?\s*([\d\s.,]+)', 'CARD', 0.95),
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# NUMERAR (cash) with amount
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(r'NUMERAR\s*:?\s*([\d\s.,]+)', 'NUMERAR', 0.95),
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# CASH alternative spelling
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(r'CASH\s*:?\s*([\d\s.,]+)', 'NUMERAR', 0.90),
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# Truncation recovery patterns (for OCR left-margin truncation issues)
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# "RD" = truncated "CARD" (only 2 chars visible)
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(r'\bRD\s*:?\s*([\d\s.,]+)', 'CARD', 0.70),
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# "ARD" = truncated "CARD" (3 chars visible)
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(r'\bARD\s*:?\s*([\d\s.,]+)', 'CARD', 0.75),
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# "MERAR" = truncated "NUMERAR"
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(r'\bMERAR\s*:?\s*([\d\s.,]+)', 'NUMERAR', 0.70),
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]
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# Items count patterns - OCR may produce OZ instead of POZ, etc.
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# Number may be on separate line before or after the label
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# IMPORTANT: Must be specific to avoid matching product quantities like "50BUC"
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@@ -246,17 +265,32 @@ class ReceiptExtractor:
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if not result.tva_entries:
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print(f"[TVA Debug] No TVA found. Checking patterns...", flush=True)
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# Debug: show what patterns see
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import re
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normalized = re.sub(r'(\d+)[.,]\s+(\d{2})', r'\1.\2', text_upper)
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taxe_match = re.search(r'T?OTAL\s+TAXE', normalized, re.IGNORECASE)
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rev_match = re.search(r'([\d.,]+)\s*T?OTAL\s+TAXE', normalized, re.IGNORECASE)
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print(f"[TVA Debug] 'OTAL TAXE' found: {bool(taxe_match)}, reversed: {rev_match.group(1) if rev_match else None}", flush=True)
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# Log TVA vs TOTAL for debugging (validation happens in ocr_service._final_validation)
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# NOTE: We NO LONGER clear TVA here - the service will recalculate TOTAL from TVA if needed
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if result.tva_total and result.amount:
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if result.tva_total > result.amount:
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print(f"[TVA Extraction] TVA ({result.tva_total}) > TOTAL ({result.amount}) - will be corrected in final validation", flush=True)
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elif result.tva_total > result.amount * Decimal('0.5'):
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print(f"[TVA Extraction] Warning: TVA ({result.tva_total}) is > 50% of TOTAL ({result.amount}) - suspicious", flush=True)
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result.items_count = self._extract_items_count(text_upper)
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result.address = self._extract_address(text_upper)
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result.payment_methods = self._extract_payment_methods(text_upper)
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# Detect receipt type
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result.receipt_type = self._detect_receipt_type(text_upper)
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# Reverse TVA validation
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if result.tva_entries and result.amount:
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is_valid, expected_total, msg = self._validate_tva_reverse(result.tva_entries, result.amount)
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if not is_valid:
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print(f"[TVA Reverse Validation] {msg}", flush=True)
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return result
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def _extract_amount(self, text: str) -> Tuple[Optional[Decimal], float]:
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@@ -892,10 +926,18 @@ class ReceiptExtractor:
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except (ValueError, InvalidOperation):
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continue
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# Calculate total
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tva_total = None
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# Extract TOTAL TVA BON as reference (separate from individual entries)
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tva_bon_total = self._extract_total_tva_bon(normalized_text)
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# Calculate sum from entries
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entries_sum = None
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if tva_entries:
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tva_total = sum(entry['amount'] for entry in tva_entries)
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entries_sum = sum(entry['amount'] for entry in tva_entries)
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# Validate and correct TVA values
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tva_entries, tva_total = self._validate_and_correct_tva(
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tva_entries, entries_sum, tva_bon_total
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)
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# Sort by code (A, B, C, D)
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tva_entries.sort(key=lambda x: x.get('code', 'Z'))
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@@ -929,6 +971,123 @@ class ReceiptExtractor:
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else:
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return 'A' # Default to standard rate
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def _extract_total_tva_bon(self, text: str) -> Optional[Decimal]:
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"""
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Extract TOTAL TVA BON value separately as the reference.
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This is the authoritative total TVA on the receipt.
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Handles OCR variations: TOTAL TVA BON, OTAL TUA BON, etc.
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"""
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# Pattern for TOTAL TVA BON with amount after
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patterns = [
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# Standard: TOTAL TVA BON: 14.92
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r'T?OTAL\s+T[VU][AR]\s+BON\s*:?\s*([\d]+[.,]\d{2})\b',
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# Amount before: 14.92 OTAL TUA BON (OCR line break)
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r'([\d]+[.,]\d{2})\s*\n?\s*T?OTAL\s+T[VU][AR]\s+BON',
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# Amount on next line after TOTAL TVA BON
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r'T?OTAL\s+T[VU][AR]\s+BON\s*\n\s*([\d]+[.,]\d{2})\b',
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]
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for pattern in patterns:
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match = re.search(pattern, text, re.IGNORECASE)
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if match:
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try:
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amount_str = self._normalize_number(match.group(1))
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amount = Decimal(amount_str)
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if amount > 0:
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return amount
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except (InvalidOperation, ValueError):
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continue
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return None
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def _validate_and_correct_tva(
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self,
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tva_entries: List[dict],
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entries_sum: Optional[Decimal],
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tva_bon_total: Optional[Decimal]
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) -> Tuple[List[dict], Optional[Decimal]]:
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"""
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Validate and correct TVA values.
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Rules:
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1. TVA cannot be greater than TOTAL amount (will be validated at higher level)
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2. Sum of TVA A + TVA B + ... should equal TOTAL TVA BON
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3. If single entry and sum != tva_bon_total, use tva_bon_total
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4. Detect and fix OCR concatenation errors (e.g., 14.921492 from 14.92 + 14.92)
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"""
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if not tva_entries:
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return tva_entries, tva_bon_total
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# Check for OCR concatenation errors in individual entries
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# Pattern: X.XX followed by another decimal (e.g., 14.921492 from 14.92 + 14.92)
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corrected_entries = []
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for entry in tva_entries:
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amount = entry['amount']
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amount_str = str(amount)
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# Check if amount looks like concatenated decimals
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# e.g., 14.921492 could be 14.92 + 14.92 incorrectly joined
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# or 32.3132.31 from 32.31 + 32.31
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if len(amount_str) > 6 and '.' in amount_str:
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int_part, dec_part = amount_str.split('.')
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# If decimal part > 2 digits, it's likely concatenation
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if len(dec_part) > 2:
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# Try to extract the first valid decimal amount
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# e.g., from 14.921492, extract 14.92
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try:
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corrected_amount = Decimal(f"{int_part}.{dec_part[:2]}")
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print(f"[TVA Validation] Corrected concatenation error: {amount} → {corrected_amount}", flush=True)
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entry['amount'] = corrected_amount
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except InvalidOperation:
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pass
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corrected_entries.append(entry)
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tva_entries = corrected_entries
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# Recalculate sum after corrections
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entries_sum = sum(entry['amount'] for entry in tva_entries) if tva_entries else None
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# Validate sum against TOTAL TVA BON
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if tva_bon_total and entries_sum:
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# Allow small tolerance for rounding (0.02)
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tolerance = Decimal('0.02')
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difference = abs(entries_sum - tva_bon_total)
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if difference > tolerance:
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print(f"[TVA Validation] Sum mismatch: entries_sum={entries_sum}, tva_bon_total={tva_bon_total}", flush=True)
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# If single entry and sum doesn't match, use TOTAL TVA BON as reference
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if len(tva_entries) == 1:
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print(f"[TVA Validation] Single entry - using TOTAL TVA BON as reference: {tva_bon_total}", flush=True)
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tva_entries[0]['amount'] = tva_bon_total
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entries_sum = tva_bon_total
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# If multiple entries and sum > tva_bon_total, likely double counting
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elif entries_sum > tva_bon_total:
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# Check if one entry is the duplicate of another
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amounts = [e['amount'] for e in tva_entries]
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unique_amounts = set(amounts)
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if len(unique_amounts) < len(amounts):
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# Duplicate detected - likely TOTAL TVA BON counted as separate entry
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print(f"[TVA Validation] Duplicate TVA detected, removing duplicates", flush=True)
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# Keep only unique entries
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seen = set()
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unique_entries = []
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for entry in tva_entries:
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key = (entry.get('code'), entry['amount'])
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if key not in seen:
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seen.add(key)
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unique_entries.append(entry)
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tva_entries = unique_entries
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entries_sum = sum(e['amount'] for e in tva_entries)
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# Final total
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tva_total = entries_sum if entries_sum else tva_bon_total
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return tva_entries, tva_total
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def _detect_tva_percent(self, text: str) -> Optional[int]:
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"""Detect TVA percentage from text content."""
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# Look for common Romanian TVA percentages
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@@ -944,6 +1103,48 @@ class ReceiptExtractor:
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return 5
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return None
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def _validate_tva_reverse(
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self,
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tva_entries: List[dict],
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total_amount: Optional[Decimal]
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) -> Tuple[bool, Optional[Decimal], str]:
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"""
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Reverse TVA validation: from TVA amount and rate, calculate expected total.
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Formula:
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base = tva_amount / (rate/100)
|
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expected_total = sum(base + tva_amount) for all entries
|
||||
|
||||
Returns (is_valid, expected_total, message)
|
||||
"""
|
||||
if not tva_entries or not total_amount:
|
||||
return True, None, "Insufficient data for reverse validation"
|
||||
|
||||
expected_total = Decimal('0')
|
||||
for entry in tva_entries:
|
||||
tva_amount = entry['amount']
|
||||
rate = Decimal(str(entry['percent']))
|
||||
|
||||
if rate > 0:
|
||||
# Calculate base from TVA: base = tva / (rate/100)
|
||||
base = tva_amount / (rate / Decimal('100'))
|
||||
expected_total += base + tva_amount
|
||||
else:
|
||||
# 0% TVA - can't calculate base, skip
|
||||
pass
|
||||
|
||||
if expected_total == 0:
|
||||
return True, None, "Cannot calculate expected total (0% TVA only)"
|
||||
|
||||
# Tolerance: max(0.50 RON, 1% of total)
|
||||
tolerance = max(Decimal('0.50'), total_amount * Decimal('0.01'))
|
||||
difference = abs(expected_total - total_amount)
|
||||
|
||||
if difference <= tolerance:
|
||||
return True, expected_total, f"TVA reverse validation passed (expected: {expected_total}, actual: {total_amount}, diff: {difference})"
|
||||
else:
|
||||
return False, expected_total, f"TVA reverse validation WARNING: expected {expected_total}, actual {total_amount}, diff {difference}"
|
||||
|
||||
def _extract_items_count(self, text: str) -> Optional[int]:
|
||||
"""Extract number of items/articles from receipt."""
|
||||
for pattern, _ in self.ITEMS_COUNT_PATTERNS:
|
||||
@@ -994,3 +1195,45 @@ class ReceiptExtractor:
|
||||
return address if len(address) >= 5 else None
|
||||
|
||||
return None
|
||||
|
||||
def _extract_payment_methods(self, text: str) -> List[dict]:
|
||||
"""
|
||||
Extract payment methods (CARD/NUMERAR) from receipt.
|
||||
These appear after TOTAL LEI and before TOTAL TVA section.
|
||||
|
||||
Returns list of: {'method': 'CARD'/'NUMERAR', 'amount': Decimal}
|
||||
"""
|
||||
payment_methods = []
|
||||
seen_methods = set()
|
||||
|
||||
# Normalize spaces in numbers
|
||||
normalized_text = re.sub(r'(\d+)[.,]\s+(\d{2})', r'\1.\2', text)
|
||||
|
||||
# Find the region between TOTAL LEI and TOTAL TVA
|
||||
total_lei_match = re.search(r'TOTAL\s+LEI\s*([\d\s.,]+)', normalized_text, re.IGNORECASE)
|
||||
total_tva_match = re.search(r'TOTAL\s+T[VU][AR]', normalized_text, re.IGNORECASE)
|
||||
|
||||
# Define search region (after TOTAL LEI, before TOTAL TVA if exists)
|
||||
if total_lei_match:
|
||||
start_pos = total_lei_match.end()
|
||||
end_pos = total_tva_match.start() if total_tva_match else len(normalized_text)
|
||||
search_region = normalized_text[start_pos:end_pos]
|
||||
else:
|
||||
search_region = normalized_text # Fallback to full text
|
||||
|
||||
for pattern, method, confidence in self.PAYMENT_METHOD_PATTERNS:
|
||||
for match in re.finditer(pattern, search_region, re.IGNORECASE):
|
||||
try:
|
||||
amount_str = match.group(1).replace(' ', '')
|
||||
amount_str = self._normalize_number(re.sub(r'[^\d.,]', '', amount_str))
|
||||
amount = Decimal(amount_str)
|
||||
if amount > 0 and method not in seen_methods:
|
||||
payment_methods.append({
|
||||
'method': method,
|
||||
'amount': amount
|
||||
})
|
||||
seen_methods.add(method)
|
||||
except (InvalidOperation, ValueError):
|
||||
continue
|
||||
|
||||
return payment_methods
|
||||
|
||||
Reference in New Issue
Block a user